Medulloblastomas are the most common malignant brain tumors in children. More than one third of children with these tumors do not survive and essentially 100% of survivors have life changing neurocognitive sequelae. New approaches to treatment with reduced toxicity are needed. In the first five years of this project, the largest medulloblastoma genomic dataset created to date was established. Using integrated analysis of molecular, genomic and clinical data, medulloblastomas were discovered to consist of multiple molecular subtypes, each with a unique molecular signature reflecting underlying mechanisms of tumorigenesis and correlated with clinical outcome. These data were used to develop the most accurate risk-stratification schema developed to date, which proved to be generalizable to a fully independent dataset. The goals of the next funding period will be to define molecular subtypes at a deeper level, to identify molecular markers for targeted therapies, and to complete the development of our risk stratification model with a goal of translating to a test with that will be incorporated into the next generation of medulloblastoma clinical trials. These goals will be accomplished through the following Specific Aims: 1) Refine and validate medulloblastoma subtyping and outcome prediction in large-scale multi-institutional prospective clinical trials;2) Implement a "real time" test for risk stratification and molecular subtyping of medulloblastoma patients;and 3) Develop an interactive website, or portal, which will provide a single, web-based, publicly available gateway to deliver genomic data, genesets, and computational methodology to the general clinical and scientific community. PUBLIC HEALTH RELEVANCE: The goal of this project is to understand how specific intrinsic molecules are related to the clinical behavior of medulloblastomas, the most common malignant brain tumors in children. Molecular markers will be developed to predict outcome so that conventional chemotherapy and radiation treatments can be optimized for maximal efficacy and to minimize damage to the developing brain. Molecular mechanisms will be identified to develop effective targeted therapies that may ultimately replace conventional treatments.